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Methodology for Formulating Diesel Surrogate Fuels

Methodology for Formulating Diesel Surrogate Fuels with Accurate Compositional, Ignition-Quality, and Volatility Characteristics.

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Methodology for Formulating Diesel Surrogate Fuels

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  1. Methodology for Formulating Diesel Surrogate Fuels with Accurate Compositional, Ignition-Quality, and Volatility Characteristics C.J. Mueller1, W.J. Cannella2, T.J. Bruno3, B. Bunting4, H.D. Dettman5, the late J.A. Franz6, M.L. Huber3, M. Natarajan7, W.J. Pitz8, M.A. Ratcliff9, and K. Wright10 1Sandia NL, 2Chevron, 3NIST, 4Oak Ridge NL, 5CanmetENERGY, 6Pacific Northwest NL, 7Marathon, 8Lawrence Livermore NL,9National Renewable Energy Lab, 10ConocoPhillips Research Supported byUS DOE Office of Vehicle Technologies, Program Mgr. Kevin Stork, andCoordinating Research Council (CRC) Advanced Engine Combustion Working Group Meeting Sandia National Laboratories, Livermore, CaliforniaFebruary 14, 2012

  2. Overall Objective Establish diesel surrogate fuels: • As time-invariant, realistic reference fuels • To better understand fuel-composition and property effects on engine processes • Ultimately to enable computational engine optimization for evolving real fuels

  3. Target fuel A “real” fuel with selected properties that are to be matched by a surrogate fuel Surrogate fuel Fuel composed of a small number of pure compounds that is formulated to match selected properties of a target fuel Design properties Selected properties of the target fuel that are to be matched by the surrogate fuel Surrogate palette The set of pure compounds that are blended together to create a surrogate fuel Terminology 3

  4. Surrogate Formulation Process Overview 4

  5. FD9A = CRC FACE Diesel #9, Batch A Target Fuels: FD9A 5 Source: GC-FIMS and PIONA data from C. Fairbridge’s group at CanmetENERGY Anomalously high levels of C8 and C9 mono-aromatics

  6. CFA = 2007 #2 ULSD Emissions Certification Fuel, Batch A Target Fuels: CFA 6 Source: GC-FIMS and PIONA data from C. Fairbridge’s group at CanmetENERGY CFA exhibits a more-typical distribution of HC type with carbon #

  7. Quantified using 13C and 1H nuclear magnetic resonance (NMR) spectroscopy techniques NMR gives structural information on a per-carbon-atom basis, whereas GC techniques give info on a per-molecule basis More-detailed structural information from NMR characterization is expected to correlate better with emissions Measurements conducted at CanmetENERGY (H. Dettman) and PNNL (J. Franz) Surrogate Design Properties: Composition 7 GC: “This is an aromatic molecule.” NMR: “37.5mol% of the carbon in this molecule has aromatic characteristics. The rest has linear-alkane characteristics.”

  8. Carbon Types from NMR Analysis 8

  9. Ignition quality Quantified using derived cetane number (DCN) per ASTM D 6890 Measurements conducted at NREL (M. Ratcliff) Excellent repeatability ± 0.7 DCN vs. method repeatability ± 2.6 DCN Volatility Quantified using advanced distillation curve (ADC) Developed by Tom Bruno and co-workers at NIST Superior to ASTM D 86 ADC gives actual thermo-dynamic state points, analogous to normal boiling points D 86 vapor temperatures underpredict liquid boiling temperatures Surrogate Design Properties: Ignition Quality and Volatility 9

  10. Surrogate Palette, “Version 1” 10 Not very representative of diesel iso-alkanes Version 1 palette contains all of the major hydrocarbon classes that are present in the target fuels

  11. Approach developed at NIST (M. Huber) Minimizes objective function S, where NCT is the number of carbon types, NADC is the number of ADC points, each W is a weighting factor, and each F is a normalized difference between predicted and target values Assumes DCN of mixture = volume-fraction-weighted sum of DCNs of palette compounds This is a reasonably accurate assumption (see Slide 15) ADC calculated using an equation-of-state based mixture model Regression Model 11

  12. MT = Measured for Target fuel, PS = Predicted for Surrogate fuel Carbon Types 6-10 are well matched Agreement of other carbon types could be improved Composition Matching: CFA 12

  13. Same trends evident with FD9A as were seen with CFA Too much n-alkane C (CTs 1&2) and quaternary aliphatic C (CT11) Not enough iso- and cyclo-alkane C (CTs 3-5) Composition Matching: FD9A 13

  14. Initially, Measured Surrogate (MS) DCNs that were 3.4 to 4.0 numbers higher than MT values Passing surrogates through baked silica gel (i.e., “silica-gel treatment,” SGT) mitigated this issue somewhat Presumably, ignition-accelerating contaminants were removed Ignition-Quality Matching 14

  15. Volume-fraction-weighted estimate is closest to measured value Other potential causes of DCN disagreement Non-linear blending effects Uncertainties in palette-compound DCNs Evaluation of Volumetric Linear Blending Assumption for DCN Prediction 15

  16. Surrogates are generally more volatile than their corresponding target fuels Volatility Matching 16

  17. Two 8-component diesel surrogate fuels were created Each surrogate contains all of the major hydrocarbon classes found in its corresponding target fuel n-alkanes; iso-alkanes; mono- and di-cyclo-alkanes; mono- and di-aromatics; and naphtho-aromatics Good matching of property targets was achieved 5 of 11 carbon types were matched within ± 3 mol%, error in others averaged 7.3 mol% Surrogate DCNs averaged 3.9% higher (post silica-gel treatment) Surrogate ADC points averaged 2.1% lower Surrogate densities averaged 4.1% lower Awareness of ignition-accelerating contaminants is important Silica-gel treatment (or similar) is recommended for groups measuring ignition delays in other venues (shock tubes, RCMs,…) Summary 17

  18. John Luecke (NREL) RafalGieleciak, Darcy Hager, Sara Salmon, Craig Fairbridge (CanmetENERGY) Mikhail Alnajjar, Tim Bays (PNNL) Chris Tennant, Brent Bailey, Jane Beck (CRC) Scott Jorgensen (GM) Krystal Wrigley (ExxonMobil) Bill Leppard (consultant) Acknowledgments 18

  19. Current/Future Work • Diesel surrogates • Refine surrogate-formulation methodology • Improved palette • Improved property modeling • Single-cylinder engine testing • CFD modeling for mechanism validation and/or refinement • Optical-engine laboratory • Diesel-fuel property effects on combustion, emissions, efficiency, and liquid length under mixing-controlled combustion conditions • FACE Diesels #1, #2, #6, and #8 (possibly others), CFA, surrogates, oxygenates (methyl esters, ethers) • Soot under mixing-controlled combustion conditions • In-cylinder soot-reduction strategies (e.g.,leaner lifted-flame combustion) • Formation, in-cylinder distribution, oxidation

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